Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges

• URL: http://arxiv.org/abs/2512.24172v1
• Date: Tue, 30 Dec 2025 12:10:43 GMT
• Title: Deep Global Clustering for Hyperspectral Image Segmentation: Concepts, Applications, and Open Challenges
• Abstract summary: Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory.<n>This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation.<n>DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware.
• Score: 1.9116784879310027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral imaging (HSI) analysis faces computational bottlenecks due to massive data volumes that exceed available memory. While foundation models pre-trained on large remote sensing datasets show promise, their learned representations often fail to transfer to domain-specific applications like close-range agricultural monitoring where spectral signatures, spatial scales, and semantic targets differ fundamentally. This report presents Deep Global Clustering (DGC), a conceptual framework for memory-efficient HSI segmentation that learns global clustering structure from local patch observations without pre-training. DGC operates on small patches with overlapping regions to enforce consistency, enabling training in under 30 minutes on consumer hardware while maintaining constant memory usage. On a leaf disease dataset, DGC achieves background-tissue separation (mean IoU 0.925) and demonstrates unsupervised disease detection through navigable semantic granularity. However, the framework suffers from optimization instability rooted in multi-objective loss balancing: meaningful representations emerge rapidly but degrade due to cluster over-merging in feature space. We position this work as intellectual scaffolding - the design philosophy has merit, but stable implementation requires principled approaches to dynamic loss balancing. Code and data are available at https://github.com/b05611038/HSI_global_clustering.

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